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Automatic Extraction of Heat Maps and Goal Instances of a Basketball Game Using Video Processing

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Computer Science – CACIC 2021 (CACIC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1584))

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Abstract

The use of image processing techniques and computer vision to obtain teams statistics in different sports, currently represents a new source of information very useful for season preparedness. In this work, we propose different methods to show the players position for a specific time, to perform the point counting, and to extract clips of goal situations in the match. In order to accomplish this, we use a combination of transfer learning using a pre-trained deep neural network with a database of basketball game excerpts, and video processing techniques. As a proof of concept, the method was applied to a basketball game of local teams, showing the feasibility of the proposed approach.

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Acknowledgement

The authors would like to thank the institute and the UNL (with CAI+D 50620190100145LI), to Enzo Ferrante and Eric Priemer by the collaboration in the original conference work, and Ryan Werth for providing the corpus of images to train the network.

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Correspondence to Gerónimo Eberle .

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Eberle, G., Bourlot, J., Martínez, C., Albornoz, E.M. (2022). Automatic Extraction of Heat Maps and Goal Instances of a Basketball Game Using Video Processing. In: Pesado, P., Gil, G. (eds) Computer Science – CACIC 2021. CACIC 2021. Communications in Computer and Information Science, vol 1584. Springer, Cham. https://doi.org/10.1007/978-3-031-05903-2_7

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  • DOI: https://doi.org/10.1007/978-3-031-05903-2_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05902-5

  • Online ISBN: 978-3-031-05903-2

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